1. Field of the Invention
The present disclosure generally relates to the field of fabricating semiconductor devices, and, more particularly, to process control and monitoring techniques for manufacturing processes, wherein an improved process control quality is achieved by detecting process failures on the basis of production data.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of semiconductor fabrication, since, here, it is essential to combine cutting-edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve product quality and process tool utilization. The latter aspect is especially important since, in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total production costs. For example, in manufacturing modern integrated circuits, several hundred individual processes may be necessary to complete the integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is even exacerbated in that the size of substrates, on which a plurality of such integrated circuits are processed, steadily increases, so that failure in a single process step may possibly entail the loss of a large number of products.
Therefore, the various manufacturing stages have to be thoroughly monitored to avoid undue waste of man power, tool operation time and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications, which would desirably have well-understood correlations to the final product quality, were met. A corresponding process control, however, is not practical since measuring the effects of certain processes may require relatively long measurement times, frequently ex-situ, or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tool would be minimized since the tool would be released only after the provision of the measurement result and its assessment. Furthermore, many of the complex mutual dependencies of the various processes are typically not known, so that a priority determination of respective process specifications may be difficult.
The introduction of statistical methods, also referred to as statistical process control (SPC), for adjusting process parameters, significantly relaxes the above problem and allows a moderate utilization of the process tools while attaining a relatively high product yield. Statistical process control is based on the monitoring of the process output to thereby identify an out-of-control situation, wherein a causality relationship may be established to an external disturbance. After occurrence of an out-of-control situation, operator interaction is usually required to manipulate a process parameter so as to return to an in-control situation, wherein the causality relationship may be helpful in selecting an appropriate control action. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein tolerable parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not efficiently be compensated for by SPC techniques.
Recently, a process control strategy has been introduced and is continuously being improved, allowing enhanced efficiency of process control, desirably on a run-to-run basis, while requiring only a moderate amount of measurement data. In this control strategy, the so-called advanced process control (APC), a model of a process or of a group of interrelated processes is established and implemented in an appropriately configured process controller. The process controller also receives information including pre-process measurement data and/or post-process measurement data, as well as information related, for instance, to the substrate history, such as type of process or processes, the product type, the process tool or process tools, in which the products are to be processed or have been processed in previous steps, the process recipe to be used, i.e., a set of required sub-steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained, and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration.
Even though APC strategies may contribute significantly to yield improvement and/or enhanced device performance and/or a reduction of production costs, nevertheless, a statistical probability exists that even process results obtained by using an APC technique may be outside of predefined value ranges, thereby resulting in yield loss. In high-volume production lines, even short delays between the occurrence of an out-of-control situation, indicating for instance an equipment failure, and its detection may therefore lead to substantial monetary losses. Consequently, it may be advantageous to apply fault detection and classification (FDC) techniques in combination with other control strategies, such as APC and/or SPC, so as to detect even subtle variations of the process sequence or the overall process, since the non-detected shift of the process may result in a large number of semiconductor devices of insufficient quality.
In conventional fault detection and classification techniques, a very large number of process parameters may have to be monitored and analyzed in order to detect a deviation from a target behavior of the manufacturing environment under consideration. As previously explained, several hundred process steps may typically be required for completing sophisticated integrated circuits, wherein each of these steps has to be maintained within specified process margins, wherein, however, the mutual interaction of the highly complex manufacturing processes on the finally obtained electrical performance of the completed device may not be known. Consequently, even a deviation of the plurality of processes within the specified process windows may result in a significant variation of the finally obtained process result. For this reason, a plurality of metrology steps are typically incorporated into the overall manufacturing flow, wherein, due to overall throughput and in view of data processing capability, typically a selected number of sample substrates may be subjected to measurement, based on which appropriate control mechanisms may be performed and also the overall quality of manufacturing sequences may be evaluated with respect to any faults. Moreover, a certain classification of detected faults may also be accomplished on the basis of the sample measurements. Although the respective measurement steps may be restricted to a defined number of samples, the continuously increasing complexity of the overall manufacturing process may require the monitoring of a large number of process parameters, such as layer thicknesses of critical process layers, such as the gate dielectric material and the like, critical dimensions of certain circuit components, such as gate electrodes, doping levels, strain levels, sheet resistivity and the like, wherein many of these process parameters may have to be monitored for a plurality of different device levels, for instance for a plurality of metallization levels and the like. Consequently, it may be extremely difficult to reliably evaluate the quality of a production process, since taking into consideration only a restricted number of process parameters may result in a less meaningful estimation since the mutual interactions of the various process steps may not be known in advance, while monitoring a high number of process parameters may involve complex data processing algorithms so as to detect relevant parameters and their deviation from target values on the basis of very large data sets.
For this reason, efficient statistical data processing algorithms may be used, which may enable a significant reduction of the high dimensionality of the parameter space, while substantially not losing valuable information on the intrinsic characteristics of the overall process flow, which may be encoded into the measurement data in a more or less subtle manner. One powerful tool for evaluating a large number of measurement data relating to a large number of parameters is the principle component analysis, which may be used for efficient data reduction. Typically, the principal component analysis (PCA) may be used for fault detection and classification by establishing a “model” of the process sequence under consideration, in that appropriately selected measurement data, which may act as reference data, may be used to identify respective “new” parameters as a linear combination of the many process parameters under consideration, wherein the new parameters or principal components may represent respective entities having the most influence on the variability of the input process parameters. Thus, typically, a significantly reduced number of new parameters may be identified which may be “monitored” in order to detect a deviation in measurement data obtained on the basis of the high dimensional parameter space. When the initial measurement data, for which a corresponding data reduction may have been performed, are considered “good” data, the respective transformations and correlation and co-variance components may be used as a model, which may be applied to other measurement data relating to the same set of parameters in order to determine deviation between the model prediction and the current measurement data. When a corresponding deviation is detected, the measurement data evaluated by the PCA model may thus be indicated as referring to a faulty state of the manufacturing environment. A corresponding deviation may be determined on the basis of statistical algorithms, as will be explained later on in more detail, so that the PCA model in combination with the statistical algorithms may allow an efficient detection and also classification of the status of the manufacturing environment corresponding to the available measurement data.
Although the PCA algorithm provides a powerful tool for detecting faults during the production of semiconductor devices, the number of parameters to be monitored may steadily increase due to the increasing complexity of the overall manufacturing flow, as previously explained. However, the model size of the PCA models increases quadratically in relation to the number of parameters used in the model, since typically respective mutual correlations are to be used in the PCA algorithm. That is, doubling the number of parameters will increase the size of the PCA model four-fold. The increase in model size, however, results in an increase of time and computer memory required to build and update the PCA models. Consequently, due to the increased number of process steps involved and the increased complexity of the semiconductor equipment, as well as the finally obtained products, an increasing number of parameters has to be monitored, thereby also contributing to an even greater increase of the corresponding PCA models. Due to the limited resources with respect to storage space and computational power, the creation and updating of PCA models may thus require extremely large resources, thereby rendering the entire PCA strategy for fault detection and classification less attractive.
For this reason, other algorithms are typically used for multivariate fault detection wherein two popular algorithms include the “k” nearest neighbor (KNN) approach and ordinary multivariate analysis (OMA). The KNN model sizes are generally smaller than the respective PCA models but are computationally more demanding and thus require increased computational resources. Furthermore, the results of KNN fault detection mechanisms are often considerably different compared to the results obtained by PCA, and the interpretation of KNN results is less comprehensive compared to the PCA results.
On the other hand, OMA has the advantage of being computationally efficient and thus inexpensive while, however, the fault detection method may not be as robust compared to PCA mechanisms. A correlation between at least some of the measured parameters is common in semiconductor manufacturing processes and this is the reason why the OMA method may create many “false alarms.”
The present disclosure is directed to various methods and systems that may avoid, or at least reduce, the effects of one or more of the problems identified above.